An Innovative Approach to Multi-Method Integrated Assessment Modelling of Global Climate Change Peer-Olaf Siebers1, Zhi En Lim 1, Grazziela P. Figueredo1, James Hey1 1School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, United Kingdom Correspondence should be addressed to [email protected] Journal of Artificial Societies and Social Simulation 23(1) 10, 2020 Doi: 10.18564/jasss.4209 Url: http://jasss.soc.surrey.ac.uk/23/1/10.html Received: 30-04-2018 Accepted: 10-12-2019 Published: 31-01-2020 Abstract: Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neo- classical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, oen used in the field of Social Simulation. But simulating huge numbers of individ- ual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers. Keywords: Integrated Assessment Modelling, Climate Change, Agent-Based Modelling, System Dynamics Mod- elling, Methodological Advance, Hybridisation, Scalability Introduction 1.1 Global warming has been a profound indicator of human-induced climate change since the mid-20th century. According to Intergovernmental Panel on Climate Change (IPCC 2014), each of the three decades preceding 2014 has been successively warmer at the Earth’s surface than any prior decade since 1850. This has led to extreme heat waves and changes in precipitation pattern occurring more frequently. Numerous evidences led IPCC to conclude that human-induced greenhouse emissions are extremely likely to have been the dominant cause of the observed warming. Scientific estimates dier about the intensity of eects, but as we allow the warming to continue, we are facing the risk of the climate crossing the tipping point where any further changes will be irreversible (Lemoine & Traeger 2014). 1.2 In response, governmental bodies and international organisations have started to promote climate change mit- igation actions, which involve substantial emissions reduction over the decades succeeding 2014; in addition, ideas regarding geo-engineering (Craig & Burns 2016; Roshan et al. 2019) have been introduced. Significant international joint eorts on this matter include the Kyoto Protocol, which was adopted in 1997, the Cancun Agreement, which was established in 2010, and the Paris agreement, ratified in 2015. The latter is eectively replacing the Kyoto Protocol. All movements share the common objective of reducing the carbon footprint of the world. In Paris the participating parties agreed to JASSS, 23(1) 10, 2020 http://jasss.soc.surrey.ac.uk/23/1/10.html Doi: 10.18564/jasss.4209 Holding the increase in the global average temperature to well below 2◦C above pre-industrial lev- els and pursuing eorts to limit the temperature increase to 1.5◦C above pre-industrial levels, rec- ognizing that this would significantly reduce the risks and impacts of climate change (EPA 2015, Article 2a). 1.3 The implementation of the required carbon reduction policy to achieve this goal poses huge technological, economic, social and institutional challenges, also not to mention the fact that a lot of the carbon quota has already been released by now (for a lively debate on the topic see CarbonBrief 2018. The dilemma in finding the right balance between environmental and economic sustainability has proven to be a prevalent problem among politicians and policy makers. 1.4 Modelling and simulation plays an increasingly significant role in exploratory studies for informing policy mak- ers on climate change mitigation strategies. The growth in computing power allows more comprehensive and sophisticated models to be produced and to be put into use. In fact, the existing literature in the discipline is considerably mature, with robust climate models capable of forecasting the weather down to the granularity of hours. There is also considerable research being done in creating more accurate Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs, as for example DICE (Nordhaus 1992, 2017), RICE (Nordhaus & Yang 1996), and C-ROADS (Climate Interactive 2016) were created as steady state optimisation models. They are highly aggregated models that focus on the economics of global warming by assessing the costs and benefits of steps towards slowing down global warming. From a techni- cal point of view IAMs typically employ a nested structure of neoclassical production functions to represent the energy-economy system (Fiddaman 1997). The DICE model, for example, comprises a traditional economic sec- tor and a novel climate sector, which form a closed-loop interaction. IAMs hold aggregate views on variables and hence are unable to capture a finer level of details of the underlying system components. This is particularly true for humans, who are the major contributors to the natural level of global warming, and are viewed in these models as an aggregation. In reality, however, humans are independent and discrete beings with diverse be- haviours. These optimisation models also neglect the non-linear relationships between humans, which could bring about unpredictable patterns. In addition, the tightly-coupled internal components of the models pre- vent or discourage dynamic modification to their structure. As such, these models lack flexibility in modelling dierent levels of aggregation and scalability, which constitute their major limitations, considering that the risks and impacts associated with climate change are unevenly distributed, geographically and demograph- ically. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent Based (AB) modelling, oen used in the field of Social Simulation. But simulating a huge number of individuals (e.g. the whole population of a country) quickly becomes an issue, as it requires large amounts of computer memory for storing these entities as individual objects and it slows down simula- tion model execution drastically as all of these objects need to be checked against each other for updates on a regular basis. 1.5 Current IAMs do not reflect well the underlying dynamics and drivers of people’s changes in behaviour over time. A more sophisticated consideration of individual dierences within the population and their influence on the overall evolution of the system is required, as people are the true drivers of change — the ones that change things (Perez et al. 2017). Our research seeks a novel approach to the design of IAMs by combining the top-down approach used in System Dynamics (SD) modelling, where the overall system behaviour is captured through complex feedback loops, with the bottom-up approach used in AB modelling, where a system is modelled as a collection of autonomous decision-making entities. We aim to drive forward the development of hybrid IAMs by providing ideas for how to implement such models using a multi-method approach. 1.6 In this paper we present an innovative concept of a scalable hybrid modelling approach for integrated assess- ment modelling and then show with an illustrative example how this concept can be applied and what a more sophisticated population model oers in terms of potential insight. We use parts of a well-established SD inter- pretation of the DICE model, developed by Fiddaman (1997) (which we will refer to as the Fiddaman model in the remainder of the paper) to represent the general environment, including economy and climate. Within this environment we use an AB modelling approach to represent hierarchical social structures as well as groups of individuals that can interact with other groups of individuals and the environment. Finally, we use an SD model to represent a conception of the environment inside a collective conscience of those groups of individuals. The target audience this paper is aimed at are model developers that want to explore new ways of creating IAMs. 1.7 When reading this paper, please keep in mind that the focus of this paper is on a methodological advance rather than creating a complex model for predictive purposes. Our illustrative example to demonstrate the application of our conceptual framework takes the settings of the United States (US), a country that contributes to the ma- jority of the global carbon footprints and that is the largest economic power in the world. The model considers the carbon emission dynamics of individual states and its relevant economic impacts on the nation over time. JASSS, 23(1) 10, 2020 http://jasss.soc.surrey.ac.uk/23/1/10.html Doi: 10.18564/jasss.4209 Background The need for a methodological advance in Integrated Assessment Modelling 2.1 There are several papers discussing the usefulness of IAMs. Moss et al. (2010) stress the need for climate change research and assessment and supports the idea of using IAMs for this purpose, as they “improve the analysis of complex issues, such as the costs, benefits and risks of dierent policy choices and climate and socioeco- nomic futures”.